1. Technical Field
The invention relates generally to the application of an array processor for the processing of medical (scintigraphic) or other digitized time sequential images requiring deconvolution to extract a final result different and distinct from the original images, but related to information contained in the original images. Also, data having substantial Poisson noise content can be efficiently processed.
2. Background Art
The prior art teaches that extracting information from images by deconvolution is done by the transformation of the desired quantity (or image) by the use of the well known Fourier transform and subsequent arithmetic operations. The Fourier transform allows the change of an image from the spatial domain to the frequency domain, while the inverse Fourier allows transformation from the frequency domain back to the spatial domain again. In the frequency domain, the computation of deconvolution with another function also in the same frequency domain requires a simple arithmetic operation, whereas in the spatial domain the deconvolution operation would require multiple steps. Because of the added required steps in the spatial domain, deconvolution is highly inefficient as compared to deconvolution in the frequency domain, i.e. the same operation can be performed in the frequency domain more efficiently. This is why a tool that allows minimal operations to compute deconvolution is beneficial whenever a large number of images having multiple pixels need to be processed to extract information contained therein.
Furthermore, some images contain high amounts of noise that may render some Fourier based and other deconvolution methods ineffective. The present application describes a method that is highly tolerant of noise contained in images to be deconvolved.